1. Field of the Invention
The present invention relates to a system and method for detecting and tracking faces, and more particularly, a face detection and tracking system and method by which a plurality of faces are detected and tracked in real time. The method for detecting a plurality of faces in real time means a method by which digital color moving pictures are input and the image coordinates and sizes of all faces appearing in an image are output in real time.
2. Description of the Related Art
In order to improve automation and monitoring performances of prior art monitoring systems, demands for a digital monitoring system, to which a real time algorithm for detecting a plurality of faces is applied, are increasing. In particular, an algorithm which can detect a variety of faces of different races having different poses and sizes appearing on an image and can operate robustly in general even under a monitoring environment having poor lighting is needed.
Meanwhile, for more effective applications, including a monitoring system, of a face detection algorithm, the most important thing is reliable detection of a face with a variety of shapes under different environments. Among the prior art methods for detecting a face, there is a method, by which whether or not a face is included in a current search window is determined through a network (or classifier) which is trained in advance, with varying the scale of the search window at all coordinates of an image, and this method has been said to provide the most reliable detection result. Also, while the size of a face appearing in an ordinary image used in a monitoring system is small, this search-window-based method has an advantage that this small-sized face can be detected by the method. As a learning classifier, a neural network or a support vector machine (hereinafter referred to as “SVM”) is widely used for detection and recognition of a face.
However, since the amount of computations for the prior art methods is huge and the complexity of the computations is very high, it is impossible for a personal computer having an ordinary computation performance of the present time to detect a face in real time.
Recently, in order to satisfy both high reliability of face detection and real time implementation, research into methods for combining a variety of information from video have been extensively carried out. Among the methods, there is a method by which a face candidate area is first searched for by combining information on a distance from an image, which can be obtained by a stereo camera, and information on a skin color. Then a classifier based on a neural network is applied. However, in this method there is a hardware limitation that a stereo camera should be used and the classifier based on a neural network has a poor generality because it operates well only for an image from a trained database.
Another method uses a skin color and face pattern information at the same time. However, in this method, motion information is not used and a skin color is too sensitive to changes of illumination.
Also suggested is a method by which in order to improve the accuracy of a face detector based on the SVM, instead of using an input image as is, feature vectors are extracted from the image by independent component analysis (ICA) and are provided to the SVM such that a face is determined. However, though this method guarantees reliability by using image patterns, computations take much time because the retrieval is performed through comparison of patterns by moving the image patterns in units of pixels. Accordingly, real time implementation of this method is needed.